Treatment Response Evaluation of Breast Cancer after Neoadjuvant Chemotherapy and Usefulness of the Imaging Parameters of MRI and PET/CT

Research trends of neoadjuvant therapy for breast cancer: A bibliometric analysis

The approach of neoadjuvant therapy for breast cancer, which involves administering systemic treatment prior to primary surgery, has undergone substantial advancements in recent decades. This strategy is intended to reduce tumor size, thereby enabling less invasive surgical procedures and enhancing patient outcomes. This study presents a comprehensive bibliometric analysis of research trends in neoadjuvant therapy for breast cancer from 2009 to 2024. Using data extracted from the Web of Science Core Collection, a total of 3,674 articles were analyzed to map the research landscape in this field. The analysis reveals a steady increase in publication output, peaking in 2022, with the United States and China identified as the leading contributors. Key institutions, such as the University of Texas System and MD Anderson Cancer Center, have been instrumental in advancing the research on neoadjuvant therapy. The study also highlights the contributions of influential authors like Sibylle Loibl and Gunter von Minckwitz, as well as major journals such as the Journal of Clinical Oncology. Emerging research topics, including immunotherapy, liquid biopsy, and artificial intelligence, are gaining prominence and represent potential future directions for clinical applications. This bibliometric analysis provides critical insights into global research trends, key contributors, and future developments in the field of neoadjuvant therapy for breast cancer, offering a foundation for future research and clinical practice advancements.

Advances in analytical approaches for background parenchymal enhancement in predicting breast tumor response to neoadjuvant chemotherapy: A systematic review

BACKGROUND

Breast cancer (BC) continues to pose a substantial global health concern, necessitating continuous advancements in therapeutic approaches. Neoadjuvant chemotherapy (NAC) has gained prominence as a key therapeutic strategy, and there is growing interest in the predictive utility of Background Parenchymal Enhancement (BPE) in evaluating the response of breast tumors to NAC. However, the analysis of BPE as a predictive biomarker, along with the techniques used to model BPE changes for accurate and timely predictions of treatment response presents several obstacles. This systematic review aims to thoroughly investigate recent advancements in the analytical methodologies for BPE analysis, and to evaluate their reliability and effectiveness in predicting breast tumor response to NAC, ultimately contributing to the development of personalized and effective therapeutic strategies.

METHODS

A comprehensive and structured literature search was conducted across key electronic databases, including Cochrane Database of Systematic Reviews, Google Scholar, PubMed, and IEEE Xplore covering articles published up to May 10, 2024. The inclusion criteria targeted studies focusing on breast cancer cohorts treated with NAC, involving both pre-treatment and at least one post-treatment breast dynamic contrast-enhanced Magnetic Resonance Imaging (DCE-MRI) scan, and analyzing BPE utility in predicting breast tumor response to NAC. Methodological quality assessment and data extraction were performed to synthesize findings and identify commonalities and differences among various BPE analytical approaches.

RESULTS

The search yielded a total of 882 records. After meticulous screening, 78 eligible records were identified, with 13 studies ultimately meeting the inclusion criteria for the systematic review. Analysis of the literature revealed a significant evolution in BPE analysis, from early studies focusing on single time-point BPE analysis to more recent studies adopting longitudinal BPE analysis. The review uncovered several gaps that compromise the accuracy and timeliness of existing longitudinal BPE analysis methods, such as missing data across multiple imaging time points, manual segmentation of the whole-breast region of interest, and over reliance on traditional statistical methods like logistic regression for modeling BPE and pathological complete response (pCR).

CONCLUSION

This review provides a thorough examination of current advancements in analytical approaches for BPE analysis in predicting breast tumor response to NAC. The shift towards longitudinal BPE analysis has highlighted significant gaps, suggesting the need for alternative analytical techniques, particularly in the realm of artificial intelligence (AI). Future longitudinal BPE research work should focus on standardization in longitudinal BPE measurement and analysis, through integration of deep learning-based approaches for automated tumor segmentation, and implementation of advanced AI technique that can better accommodate varied breast tumor responses, non-linear relationships and complex temporal dynamics in BPE datasets, while also handling missing data more effectively. Such integration could lead to more precise and timely predictions of breast tumor responses to NAC, thereby enhancing personalized and effective breast cancer treatment strategies.

Quantitative dynamic contrast-enhanced MRI parameters effectively predict treatment efficacy of neoadjuvant chemotherapy in breast cancer

PURPOSE

To investigate the predictive value of quantitative DCE-MRI parameters for estimating the treatment efficacy of neoadjuvant chemotherapy (NACT) in breast carcinoma (BC).

METHODS

A retrospective analysis was conducted on 178 pathologically confirmed cases of BC, diagnosed via puncture biopsy, at The Second Affiliated Hospital of Anhui Medical University between January 2019 and June 2023. All patients received preoperative NACT. Based on postoperative pathological inspection results, 53 patients with grade IV-V pathological responses were included in the major histological response (MHR) group, and the remaining 125 with grade I-III pathological responses were assigned to the non-major histological response (NMHR) group. The pre- and post-chemotherapy early-phase enhancement rate (E1), peak enhancement rate (Emax), and time to peak (Tmax) on DCE-MRI were compared between the two patient cohorts. Quantitative parameters such as volume transfer constant (Ktrans), rate constant (Kep) and extravascular extracellular volume fraction (Ve) were obtained, and the post-NACT maximum tumor diameter (D-max) reduction rate and tumor volume reduction rate (TVRR) were calculated. Furthermore, the predictive efficacy of pre- and post-NACT quantitative DCE-MRI parameters for treatment responses was evaluated using receiver operating characteristic (ROC) curves.

RESULTS

The MHR group showed statistically higher post-NACT D-max reduction rate and TVRR than the NMHR group. The two patient cohorts were similar in pre-chemotherapy Kep, but the pre-chemotherapy Ktrans and Ve were lower in MHR; the post-chemotherapy Ktrans, Kep and Ve were all statistically different between groups (P < 0.05). The MHR group presented markedly lower E1 and Emax values and statistically longer Tmax compared to the NMHR group after NACT (all P < 0.05). The pre-NACT quantitative DCE-MRI parameters demonstrated limited prediction performance, with Ve showing the highest efficacy (AUC = 0.612); in contrast, post-NACT quantitative DCE-MRI parameters exhibited improved predictive accuracy, with Ktrans demonstrating the best predictive performance (AUC = 0.801).

CONCLUSIONS

The pre-NACT quantitative DCE-MRI parameters are not effective in predicting the therapeutic outcome of NACT. However, the post-NACT DCE-MRI parameters provide accurate and reliable predictions of pathological responses, with Ktrans showing the highest predictive value and considerable clinical applicability.

Cross-modal deep learning model for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer

Pathological complete response (pCR) serves as a critical measure of the success of neoadjuvant chemotherapy (NAC) in breast cancer, directly influencing subsequent therapeutic decisions. With the continuous advancement of artificial intelligence, methods for early and accurate prediction of pCR are being extensively explored. In this study, we propose a cross-modal multi-pathway automated prediction model that integrates temporal and spatial information. This model fuses digital pathology images from biopsy specimens and multi-temporal ultrasound (US) images to predict pCR status early in NAC. The model demonstrates exceptional predictive efficacy. Our findings lay the foundation for developing personalized treatment paradigms based on individual responses. This approach has the potential to become a critical auxiliary tool for the early prediction of NAC response in breast cancer patients.

PET Molecular Imaging in Breast Cancer: Current Applications and Future Perspectives

Positron emission tomography (PET) plays a crucial role in breast cancer management. This review addresses the role of PET imaging in breast cancer care. We focus primarily on the utility of 18F-fluorodeoxyglucose (FDG) PET in staging, recurrence detection, and treatment response evaluation. Furthermore, we delve into the growing interest in precision therapy and the development of novel radiopharmaceuticals targeting tumor biology. This includes discussing the potential of PET/MRI and artificial intelligence in breast cancer imaging, offering insights into improved diagnostic accuracy and personalized treatment approaches.

Impact of Imaging Biomarkers and AI on Breast Cancer Management: A Brief Review

Breast cancer stands out as the most frequently identified malignancy, ranking as the fifth leading cause of global cancer-related deaths. The American College of Radiology (ACR) introduced the Breast Imaging Reporting and Data System (BI-RADS) as a standard terminology facilitating communication between radiologists and clinicians; however, an update is now imperative to encompass the latest imaging modalities developed subsequent to the 5th edition of BI-RADS. Within this review article, we provide a concise history of BI-RADS, delve into advanced mammography techniques, ultrasonography (US), magnetic resonance imaging (MRI), PET/CT images, and microwave breast imaging, and subsequently furnish comprehensive, updated insights into Molecular Breast Imaging (MBI), diagnostic imaging biomarkers, and the assessment of treatment responses. This endeavor aims to enhance radiologists' proficiency in catering to the personalized needs of breast cancer patients. Lastly, we explore the augmented benefits of artificial intelligence (AI), machine learning (ML), and deep learning (DL) applications in segmenting, detecting, and diagnosing breast cancer, as well as the early prediction of the response of tumors to neoadjuvant chemotherapy (NAC). By assimilating state-of-the-art computer algorithms capable of deciphering intricate imaging data and aiding radiologists in rendering precise and effective diagnoses, AI has profoundly revolutionized the landscape of breast cancer radiology. Its vast potential holds the promise of bolstering radiologists' capabilities and ameliorating patient outcomes in the realm of breast cancer management.

Comparison of MRI vs. [18F]FDG PET/CT for Treatment Response Evaluation of Primary Breast Cancer after Neoadjuvant Chemotherapy: Literature Review and Future Perspectives

The purpose of this systematic review was to investigate the diagnostic accuracy of [18F]FDG PET/CT and breast MRI for primary breast cancer (BC) response assessment after neoadjuvant chemotherapy (NAC) and to evaluate future perspectives in this setting. We performed a critical review using three bibliographic databases (i.e., PubMed, Scopus, and Web of Science) for articles published up to the 6 June 2023, starting from 2012. The Quality Assessment of Diagnosis Accuracy Study (QUADAS-2) tool was adopted to evaluate the risk of bias. A total of 76 studies were identified and screened, while 14 articles were included in our systematic review after a full-text assessment. The total number of patients included was 842. Eight out of fourteen studies (57.1%) were prospective, while all except one study were conducted in a single center. In the majority of the included studies (71.4%), 3.0 Tesla (T) MRI scans were adopted. Three out of fourteen studies (21.4%) used both 1.5 and 3.0 T MRI and only two used 1.5 T. [18F]FDG was the radiotracer used in every study included. All patients accepted surgical treatment after NAC and each study used pathological complete response (pCR) as the reference standard. Some of the studies have demonstrated the superiority of [18F]FDG PET/CT, while others proved that MRI was superior to PET/CT. Recent studies indicate that PET/CT has a better specificity, while MRI has a superior sensitivity for assessing pCR in BC patients after NAC. The complementary value of the combined use of these modalities represents probably the most important tool to improve diagnostic performance in this setting. Overall, larger prospective studies, possibly randomized, are needed, hopefully evaluating PET/MR and allowing for new tools, such as radiomic parameters, to find a proper place in the setting of BC patients undergoing NAC.

Dynamic Contrast-Enhanced Magnetic Resonance Imaging for the Prediction of Monoclonal Antibody Tumor Disposition

The prediction of monoclonal antibody (mAb) disposition within solid tumors for individual patients is difficult due to inter-patient variability in tumor physiology. Improved a priori prediction of mAb pharmacokinetics in tumors may facilitate the development of patient-specific dosing protocols and facilitate improved selection of patients for treatment with anti-cancer mAb. Here, we report the use of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), with tumor penetration of the contrast agent gadobutrol used as a surrogate, to improve physiologically based pharmacokinetic model (PBPK) predictions of cetuximab pharmacokinetics in epidermal growth factor receptor (EGFR) positive xenografts. In the initial investigations, mice bearing Panc-1, NCI-N87, and LS174T xenografts underwent DCE-MRI imaging with the contrast agent gadobutrol, followed by intravenous dosing of an 125Iodine-labeled, non-binding mAb (8C2). Tumor concentrations of 8C2 were determined following the euthanasia of mice (3 h-6 days after 8C2 dosing). Potential predictor relationships between DCE-MRI kinetic parameters and 8C2 PBPK parameters were evaluated through covariate modeling. The addition of the DCE-MRI parameter Ktrans alone or Ktrans in combination with the DCE-MRI parameter Vp on the PBPK parameters for tumor blood flow (QTU) and tumor vasculature permeability (σTUV) led to the most significant improvement in the characterization of 8C2 pharmacokinetics in individual tumors. To test the utility of the DCE-MRI covariates on a priori prediction of the disposition of mAb with high-affinity tumor binding, a second group of tumor-bearing mice underwent DCE-MRI imaging with gadobutrol, followed by the administration of 125Iodine-labeled cetuximab (a high-affinity anti-EGFR mAb). The MRI-PBPK covariate relationships, which were established with the untargeted antibody 8C2, were implemented into the PBPK model with considerations for EGFR expression and cetuximab-EGFR interaction to predict the disposition of cetuximab in individual tumors (a priori). The incorporation of the Ktrans MRI parameter as a covariate on the PBPK parameters QTU and σTUV decreased the PBPK model prediction error for cetuximab tumor pharmacokinetics from 223.71 to 65.02%. DCE-MRI may be a useful clinical tool in improving the prediction of antibody pharmacokinetics in solid tumors. Further studies are warranted to evaluate the utility of the DCE-MRI approach to additional mAbs and additional drug modalities.

Application of MRI Image Based on Computer Semiautomatic Segmentation Algorithm in the Classification Prediction of Breast Cancer Histology

Objective

The study aimed to investigate the predictive classification accuracy of computer semiautomatic segmentation algorithm for the histological grade of breast tumors through the magnetic resonance imaging (MRI) examination.

Methods

Five dynamic contrast-enhanced (DCE) MRI regions of interest (ROIs) were captured using computer semiautomatic segmentation method, referring to the entire tumor area, tumor border area, proximal gland area, middle gland area, and distal gland area. According to the mutual information maximum protocol, the corresponding five ROIs were extracted from diffusion weighted imaging (DWI) combined with DCE-MRI images. To use the features in the nonoverlapping area of DWI image and DCE-MRI image as elements, a single-variable logistic regression model was established corresponding to element characteristics. After multiple training, the model was evaluated using the receiver operating characteristic (ROC) curve and area under curve (AUC).

Results

This DCE-MRI combined with DWI was superior to DCE-MRI and DW in the prediction of tumor area features. To use DCE-MRI or DWI alone was less effective than DCE-MRI combined with DWI. The DWI combined DCE-MRI demonstrated good regional segmentation effects in the tumour area, with luminal A value being 0.767 and the area under curve (AUC) value being 0.758. After optimization, the AUC value of the tumor area was 0.929, indicating that classification effects can be enhanced by combining the two imaging methods, which complemented each other.

Conclusions

The DWI combined DCE-MRI imaging has improved the early diagnosis effects of breast cancer by predicting the occurrence of breast cancer through the labeling of biomarkers.

The diagnostic performance of diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging in evaluating the pathological response of breast cancer to neoadjuvant chemotherapy: A meta-analysis

PURPOSE

To evaluate and compare the diagnostic performance of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting the pathological response of breast cancer to neoadjuvant chemotherapy (NAC).

METHODS

We searched PubMed, EMBASE, Cochrane Library, and Web of Science systematically to identify relevant studies from inception to December 2020. The Quality Assessment of Diagnostic Accuracy Studies 2 tool was used to assess the methodological quality of the included studies. We extracted sufficient data to construct 2 × 2 tables and then used STATA 12.0 to perform data pooling, heterogeneity testing, meta-regression analysis and subgroup analysis.

RESULTS

A total of 41 articles were enrolled in this study, including 27 studies (2107 patients) on DCE-MRI and 23 studies (1321 patients) on DWI. The pooled sensitivity and specificity of DCE-MRI were 0.75 and 0.79, and the pooled sensitivity and specificity of DWI were 0.77 and 0.75. There was no significant difference in sensitivity (P = 0.598) and specificity (P = 0.218) ​​between DCE-MRI and DWI. And meta-regression analysis showed that both magnetic field strength and the time of examination had significant effects on heterogeneity.

CONCLUSIONS

DWI might be a potential substitute for DCE-MRI in predicting the pathological response of breast cancer to NAC as there was no significant difference in the diagnostic performance between the two. However, considering that not all included studies directly compared the diagnostic performance of DWI and DCE-MRI in the same patients and the heterogeneity of the included studies, caution should be exercised in applying our results.

Assessment and Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer: A Comparison of Imaging Modalities and Future Perspectives

Neoadjuvant chemotherapy (NAC) is becoming the standard of care for locally advanced breast cancer, aiming to reduce tumor size before surgery. Unfortunately, less than 30% of patients generally achieve a pathological complete response and approximately 5% of patients show disease progression while receiving NAC. Accurate assessment of the response to NAC is crucial for subsequent surgical planning. Furthermore, early prediction of tumor response could avoid patients being overtreated with useless chemotherapy sections, which are not free from side effects and psychological implications. In this review, we first analyze and compare the accuracy of conventional and advanced imaging techniques as well as discuss the application of artificial intelligence tools in the assessment of tumor response after NAC. Thereafter, the role of advanced imaging techniques, such as MRI, nuclear medicine, and new hybrid PET/MRI imaging in the prediction of the response to NAC is described in the second part of the review. Finally, future perspectives in NAC response prediction, represented by AI applications, are discussed.

PET/CT-Based Response Evaluation in Cancer-a Systematic Review of Design Issues

Positron emission tomography/x-ray computed tomography (PET/CT) has long been discussed as a promising modality for response evaluation in cancer. When designing respective clinical trials, several design issues have to be addressed, especially the number/timing of PET/CT scans, the approach for quantifying metabolic activity, and the final translation of measurements into a rule. It is unclear how well these issues have been tackled in quest of an optimised use of PET/CT in response evaluation. Medline via Ovid and Science Citation Index via Web of Science were systematically searched for articles from 2015 on cancer patients scanned with PET/CT before and during/after treatment. Reports were categorised as being either developmental or evaluative, i.e. focusing on either the establishment or the evaluation of a rule discriminating responders from non-responders. Of 124 included papers, 112 (90 %) were accuracy and/or prognostic studies; the remainder were response-curve studies. No randomised controlled trials were found. Most studies were prospective (62 %) and from single centres (85 %); median number of patients was 38.5 (range 5-354). Most (69 %) of the studies employed only one post-baseline scan. Quantification was mainly based on SUVmax (91 %), while change over time was most frequently used to combine measurements into a rule (79 %). Half of the reports were categorised as developmental, the other half evaluative. Most development studies assessed only one element (35/62, 56 %), most frequently the choice of cut-off points (25/62, 40 %). In summary, the majority of studies did not address the essential open issues in establishing PET/CT for response evaluation. Reasonably sized multicentre studies are needed to systematically compare the many different options when using PET/CT for response evaluation.

Radiomic signatures derived from multiparametric MRI for the pretreatment prediction of response to neoadjuvant chemotherapy in breast cancer

Objectives:

To investigate the ability of radiomic signatures based on MRI to evaluate the response and efficiency of neoadjuvant chemotherapy (NAC) for treating breast cancers.

Methods:

152 patients were included in this study at our institution between March 2017 and September 2019. All patients with breast cancer underwent a preoperative breast MRI and the Miller–Payne grading system was applied to evaluate response to NAC. Quantitative parameters were compared between patients with sensitive and insensitive responses to NAC and between those with pathological complete responses (pCR) and non-pCR. Four radiomic signatures were built based on T2W imaging, diffusion-weighted imaging, dynamic contrast-enhanced imaging and their combination, and radiomics scores (Rad-score) were calculated. The combination of the clinical factors and Rad-scores created a nomogram model. Multivariate logistic regression was performed to assess the association between MRI features and independent clinical risk factors.

Results:

20 features and 18 features were selected to build the radiomic signature for evaluating sensitivity and the possibility of pCR, respectively. The combined radiomic signature and nomogram model showed a similar discrimination in the training (AUC 0.91, 0.92, 95% confidence interval [CI], 0.85–0.96, 0.86–0.98) and validation (AUC 0.93, 0.91, 95% CI, 0.86–1.00, 0.82–1.00) sets. The clinical factor model exhibited reduced performance (AUC 0.74, 0.64, 95% CI, 0.64–0.84, 0.46–0.82) in terms of NAC sensitivity and pCR.

Conclusions:

The combined radiomic signature and nomogram model exhibited potential predictive power for predicting effective NAC treatment which can aid in the prognosis and guidance of treatment regimens.

Advances in knowledge:

Identifying a means of assessing the efficacy of NAC before surgery can guide follow-up treatment and avoid chemotherapy-induced toxicity.

Progress and Future Trends in PET/CT and PET/MRI Molecular Imaging Approaches for Breast Cancer

Breast cancer is a major disease with high morbidity and mortality in women worldwide. Increased use of imaging biomarkers has been shown to add more information with clinical utility in the detection and evaluation of breast cancer. To date, numerous studies related to PET-based imaging in breast cancer have been published. Here, we review available studies on the clinical utility of different PET-based molecular imaging methods in breast cancer diagnosis, staging, distant-metastasis detection, therapeutic and prognostic prediction, and evaluation of therapeutic responses. For primary breast cancer, PET/MRI performed similarly to MRI but better than PET/CT. PET/CT and PET/MRI both have higher sensitivity than MRI in the detection of axillary and extra-axillary nodal metastases. For distant metastases, PET/CT has better performance in the detection of lung metastasis, while PET/MRI performs better in the liver and bone. Additionally, PET/CT is superior in terms of monitoring local recurrence. The progress in novel radiotracers and PET radiomics presents opportunities to reclassify tumors by combining their fine anatomical features with molecular characteristics and develop a beneficial pathway from bench to bedside to predict the treatment response and prognosis of breast cancer. However, further investigation is still needed before application of these modalities in clinical practice. In conclusion, PET-based imaging is not suitable for early-stage breast cancer, but it adds value in identifying regional nodal disease and distant metastases as an adjuvant to standard diagnostic imaging. Recent advances in imaging techniques would further widen the comprehensive and convergent applications of PET approaches in the clinical management of breast cancer.

The Diagnostic Performance of DCE-MRI in Evaluating the Pathological Response to Neoadjuvant Chemotherapy in Breast Cancer: A Meta-Analysis

Background: Neoadjuvant chemotherapy (NAC) is commonly utilized in preoperative treatment for local breast cancer, and it gives high clinical response rates and can result in pathologic complete response (pCR) in 6–25% of patients. In recent years, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been increasingly used to assess the pathological response of breast cancer to NAC. In present analysis, we assess the diagnostic performance of DCE-MRI in evaluating the pathological response of breast cancer to NAC.

Materials and Methods: A systematic search in PubMed, the Cochrane Library, and Web of Science for original studies was performed. The Quality Assessment of Diagnostic Accuracy Studies-2 tool was used to assess the methodological quality of the included studies. Patient, study, and imaging characteristics were extracted, and sufficient data to reconstruct 2 × 2 tables were obtained. Data pooling, heterogeneity testing, forest plot construction, meta-regression analysis and sensitivity analysis were performed using Stata version 12.0 (StataCorp LP, College Station, TX).

Results: Eighteen studies (969 patients with breast cancer) were included in the present meta-analysis. The pooled sensitivity and specificity of DCE-MRI were 0.80 (95% confidence interval [CI]: 0.70, 0.88) and 0.84 (95% [CI]: 0.79, 0.88), respectively. Meta-regression analysis found no significant factors affecting heterogeneity. Sensitivity analysis showed that studies that set pathological complete response (pCR) (n = 14) as a responder showed a tendency for higher sensitivity compared with those that set pCR and near pCR together (n = 5) as a responder (0.83 vs. 0.72), and studies (n = 14) that used DCE-MRI to early predict the pathological response of breast cancer had a higher sensitivity (0.83 vs. 0.71) and equivalent specificity (0.80 vs. 0.86) compared to studies (n = 5) that assessed the response after NAC completion.

Conclusion: Our results indicated that DCE-MRI could be considered an important auxiliary method for evaluating the pathological response of breast cancer to NAC and used as an effective method for dynamically monitoring the efficacy during NAC. DCE-MRI also performed well in predicting the pCR of breast cancer to NAC. However, due to the heterogeneity of the included studies, caution should be exercised in applying our results.

Diagnosis of spinal lesions using perfusion parameters measured by DCE-MRI and metabolism parameters measured by PET/CT

PURPOSE

To investigate the correlation of parameters measured by dynamic-contrast-enhanced MRI (DCE-MRI) and ¹⁸F-FDG PET/CT in spinal tumors, and their role in differential diagnosis.

METHODS

A total of 49 patients with pathologically confirmed spinal tumors, including 38 malignant, six benign and five borderline tumors, were analyzed. The MRI and PET/CT were done within 3 days, before biopsy. On MRI, the ROI was manually placed on area showing the strongest enhancement to measure pharmacokinetic parameters K^trans and k_ep. On PET, the maximum standardized uptake value SUV_max was measured. The parameters in different histological groups were compared. ROC was performed to differentiate between the two largest subtypes, metastases and plasmacytomas. Spearman rank correlation was performed to compare DCE-MRI and PET/CT parameters.

RESULTS

The K^trans, k_ep and SUV_max were not statistically different among malignant, benign and borderline groups (P = 0.95, 0.50, 0.11). There was no significant correlation between K^trans and SUV_max (r = - 0.20, P = 0.18), or between k_ep and SUV_max (r = - 0.16, P = 0.28). The k_ep was significantly higher in plasmacytoma than in metastasis (0.78 ± 0.17 vs. 0.61 ± 0.18, P = 0.02); in contrast, the SUV_max was significantly lower in plasmacytoma than in metastasis (5.58 ± 2.16 vs. 9.37 ± 4.26, P = 0.03). In differential diagnosis, the AUC of k_ep and SUV_max was 0.79 and 0.78, respectively.

CONCLUSIONS

The vascular parameters measured by DCE-MRI and glucose metabolism measured by PET/CT from the most aggressive tumor area did not show a significant correlation. The results suggest they provide complementary information reflecting different aspects of the tumor, which may aid in diagnosis of spinal lesions. These slides can be retrieved under Electronic Supplementary Material.

The Evolving Role of FDG-PET/CT in the Diagnosis, Staging, and Treatment of Breast Cancer

The applications of 2-deoxy-2-[¹⁸F]fluoro-D-glucose positron emission tomography/X-ray computed tomography (PET/CT) in the management of patients with breast cancer have been extensively studied. According to these studies, PET/CT is not routinely performed for the diagnosis of primary breast cancer, although PET/CT in specific subtypes of breast cancer correlates with histopathologic features of the primary tumor. PET/CT can detect metastases to mediastinal, axial, and internal mammary nodes, but it cannot replace the sentinel node biopsy. In detection of distant metastases, this imaging tool may have a better accuracy in detecting lytic bone metastases compared to bone scintigraphy. Thus, PET/CT is recommended when advanced-stage disease is suspected, and conventional modalities are inconclusive. Also, PET/CT has a high sensitivity and specificity to detect loco-regional recurrence and is recommended in asymptomatic patients with rising tumor markers. Numerous studies support the future role of PET/CT in prediction of response to neoadjuvant chemotherapy (NAC). PET/CT has a higher diagnostic value for prognostic risk stratification in comparison with conventional modalities. With the continuing research on the treatment planning and evaluation of patients with breast cancer, the role of PET/CT can be further extended.

Meta-Analysis of Quantitative Dynamic Contrast-Enhanced MRI for the Assessment of Neoadjuvant Chemotherapy in Breast Cancer

The purpose of this meta-analysis was to determine the value of quantitative dynamic contrast-enhanced (DCE) MRI (DCE-MRI) in evaluating the response of breast cancer to neoadjuvant chemotherapy (NAC). PubMed, Embase, and Cochrane Library databases (from building to July 31, 2018) were searched to collect articles about the therapeutic evaluation of NAC using the quantitative DCE-MRI in patients with breast cancer. The sensitivities and specificities of quantitative DCE-MRI in the evaluation of NAC for breast cancer were extracted from the articles. Meta-DiSc1.4 was applied to evaluate the efficacy of the sensitivity and specificity; forest figure and summary receiver operating characteristics (SROC) were created. A total of 356 articles were enrolled in this study, including 739 cases in total, in which 218 cases were effective and the other 521 cases were ineffective to NAC, considering the pathological results as the gold standard. The sensitivity and specificity in the included 14 articles of quantitative DCE-MRI ( Ktrans, kep, and ve) in comprehensively evaluating NAC for breast cancer were 84 per cent (95% confidence interval (CI): 78–88%) and 83 per cent (95% CI: 79–86%), respectively. The area under SROC was 0.899 (95% CI: 0.867–0.943). The sensitivity and specificity in the three articles of Ktrans evaluating NAC for breast cancer were 84.1 per cent (95% CI: 71.0–92.1%) and 81.3 per cent (95% CI: 70.5%-88.5%), respectively. The area under SROC was 0.899 (95% CI: 0.834–0.962). Our study confirmed that the quantitative DCE-MRI is able to monitor NAC treatment for breast cancer because of its high sensitivity and specificity. However, there is a high degree of heterogeneity in published studies, highlighting the lack of standardization in the field.

Strain wave elastography in response assessment to neo-adjuvant chemotherapy in patients with locally advanced breast cancer

OBJECTIVE

The study was conducted to study the role of strain wave elastography in evaluating the response to neo-adjuvant chemotherapy (NACT) in patients with locally advanced breast cancer (LABC).

METHODS

In this Institutional review board approved study, 86 patients of LABC were investigated with strain wave elastography. Females receiving NACT had the affected breast scanned by strain wave elastography before each cycle of chemotherapy and immediately before surgery by two independent observers. Changes in elastographic parameters (size ratio, strain ratio) were documented and then compared to clinical and pathologic tumor response as evaluated after mastectomy.

RESULTS

Elastographic strain ratio parameters demonstrated high sensitivity and moderate specificity for determining response even after the first cycle of neo-adjuvant chemotherapy [97.7% sensitivity (Sn), 68.7% specificity (Sp)]. Elastographic size ratio parameters showed moderate sensitivity and specificity for response detection after second and third cycle of neo-adjuvant chemotherapy (Sn, Sp: after second cycle of NACT Sn 83.3% Sp 80%; after third cycle of NACT Sn 77.8% Sp 100%).

CONCLUSION

Strain ratio is the earliest predictor of treatment response in patients of LABC. Serial imaging with elastography has the potential to predict treatment response early during the course of NACT, which may prove vital in management of patients with breast cancer.

ADVANCES IN KNOWLEDGE

Strain wave elastography is a powerful tool to predict chemoresponse early during the course of management, thereby providing an optimal window to change treatment protocols.

MRI and PET/CT for evaluation of the pathological response to neoadjuvant chemotherapy in breast cancer: A systematic review and meta-analysis

BACKGROUND

Neoadjuvant chemotherapy (NAC) has become an essential treatment for breast cancer. However, there is still no consensus on the best tool to evaluate pathological response to NAC.

METHODS

Two reviewers systematically searched Cochrane, PubMed, EMBASE, Web of Science, and CBM (last updated in February 2017) for eligible articles. We independently screened and selected studies that conformed to the inclusion criteria and extracted the requisite data. Pooled sensitivity, specificity, and the area under the SROC curve were calculated to estimate the diagnostic accuracy of magnetic resonance imaging (MRI) and positron emission computed tomography (PET/CT). And the relative DOR (RDOR) was used to compare accuracy for levels of the covariable.

RESULTS

Thirteen studies involving 575 patients who underwent MRI and 618 who underwent PET/CT were included in our analysis. The pooled sensitivity and specificity of MRI were 0.88 (95% CI: 0.78-0.94) and 0.69 (95% CI: 0.51-0.83), respectively. The corresponding values for PET/CT were 0.77 (95% CI: 0.58-0.90) and 0.78 (95% CI: 0.63-0.88), respectively. The area under the SROC curve for MRI and PET/CT were 0.88 and 0.84, respectively. And the RDOR = 1.44 (95% CI, 0.46-4.47 P = 0.83).

CONCLUSION

MRI had a higher sensitivity and PET/CT had a higher specificity in predicting the pathologic response after NAC in patients with breast cancer. According to the area under the SROC curve and anatomic discriminative resolution, MRI is the more suitable recommendation for predicting the pathologic response after NAC.

Multiparametric Evaluation of Treatment Response to Neoadjuvant Chemotherapy in Breast Cancer Using Integrated PET/MR

PURPOSE

The aim of this study was to investigate whether integrated PET/MR system can predict the treatment response to neoadjuvant chemotherapy (NAC) early in the course of breast cancer treatment.

METHODS

Fourteen women with newly diagnosed invasive breast cancer (median age, 54.5 years) were recruited. Each participant underwent 2 PET/MR studies. Study 1 was pre-NAC; study 2 was early in NAC treatment (after the first or second cycle). PET parameters included SUVmax and total lesion glycolysis (TLG). MRI parameters included choline signal-to-noise ratio (ChoSNR), peak enhancement ratio (PER), and the minimum apparent diffusion coefficient (ADCmin). The pathologic response was categorized as a pathologic complete response or residual cellularity of less than 10% (group 1) and residual cellularity of 10% or greater (group 2). The accuracy of the NAC response prediction was obtained by receiver operating characteristic analysis.

RESULTS

Group 1 showed a greater reduction of SUVmax (percentage change, [INCREMENT]% SUVmax, P = 0.013; area under the receiver operating characteristic curve [AUC], 0.898), TLG ([INCREMENT]%TLG, P = 0.018; AUC = 0.878), and PER ([INCREMENT]% PER, P = 0.035; AUC = 0.837) than did group 2. The ChoSNR, ADCmin, [INCREMENT]%ChoSNR, and [INCREMENT]%ADCmin did not differ significantly between the 2 groups. The hybrid markers, [INCREMENT]%SUVmax/[INCREMENT]%ADCmin (AUC = 0.976) and [INCREMENT]%TLG/[INCREMENT]%ADCmin (AUC = 0.905), showed greater accuracy in predicting NAC response than the individual PET/MR parameters.

CONCLUSIONS

The PET/MR parameters can predict the NAC response early in the course of breast cancer treatment. The hybrid markers more accurately predicted treatment response than the individual PET/MR parameters.

Diffusion-weighted imaging in identifying breast cancer pathological response to neoadjuvant chemotherapy: A meta-analysis

Background

Diffusion-weighted imaging (DWI) is increasingly used to identify pathological complete responses (pCRs) to neoadjuvant chemotherapy (NAC) in breast cancer. The aim of the present study was to assess the utility of DWI using a pooled analysis.

Materials and Methods

Literature databases were searched prior to July 2017. Fifteen studies with a total of 1181 patients were included. The data were extracted to perform pooled analysis, heterogeneity testing, threshold effect testing, sensitivity analysis, publication bias analysis and subgroup analyses.

Result

The methodological quality was moderate. Remarkable heterogeneity was detected, primarily due to a threshold effect. The pooled weighted values were a sensitivity of 0.88 (95% confidence interval (CI): 0.81, 0.92), a specificity of 0.79 (95% CI: 0.70, 0.86), a positive likelihood ratio of 4.1 (95% CI: 2.9, 5.9), a negative likelihood ratio of 0.16 (95% CI: 0.10, 0.24), and a diagnostic odds ratio of 26 (95% CI: 15, 46). The area under the receiver operator characteristic curve was 0.91 (95% CI: 0.88, 0.93). In the subgroup analysis, the pooled specificity of change in the apparent diffusion coefficient (ADC) subgroup was higher than that in the pre-treatment ADC subgroup (0.80 [95% CI: 0.71, 087] vs. 0.63 [95% CI: 0.52, 0.73], P = 0.027).

Conclusions

DWI may be an accurate and nonradioactive imaging technique for identifying pCRs to NAC in breast cancer. Nonetheless, there are a variety of issues when assessing DWI techniques for estimating breast cancer responses to NAC, and large scale and well-designed clinical trials are needed to assess the technique's diagnostic value.

Predicting Treatment Response of Breast Cancer to Neoadjuvant Chemotherapy Using Ultrasound-Guided Diffuse Optical Tomography

PURPOSE

To prospectively investigate ultrasound-guided diffuse optical tomography (US-guided DOT) in predicting breast cancer response to neoadjuvant chemotherapy (NAC).

MATERIALS AND METHODS

Eighty-eight breast cancer patients, with a total of 93 lesions, were included in our study. Pre- and post-last chemotherapy, size and total hemoglobin concentration (THC) of each lesion were measured by conventional US and US-guided DOT 1 day before biopsy (time point t0, THC THC0, SIZE S0) and 1 to 2 days before surgery (time point tL, THCL, SL). The relative changes in THC and SIZE of lesions after the first and last NAC cycles were considered as the variables ΔTHC and ΔSIZE. Receiver operating characteristic curve was performed to calculate ΔTHC and ΔSIZE cutoff values to evaluate pathologic response of 93 breast cancers to NAC, which were then prospectively used to predicate response of 61 breast cancers to NAC.

RESULTS

The cutoff values of ΔTHC and ΔSIZE for evaluation of breast cancers NAC treatment response were 23.9% and 42.6%. At ΔTHC 23.9%, the predicted treatment response in 61 breast lesions for the time points t1 to t3 was calculated by area under the curve (AUC), which were AUC₁ 0.534 (P=.6668), AUC₂ 0.604 (P=.1893), and AUC₃ 0.674(P =. 0.027), respectively; for ΔSIZE 42.6%, at time points t1 to t3, AUC₁ 0.505 (P=.9121), AUC₂ 0.645 (P=.0115), and AUC₃ 0.719 (P=.0018).

CONCLUSION

US-guided DOT ΔTHC 23.9% and US ΔSIZE 42.6% can be used for the response evaluation and earlier prediction of the pathological response after three rounds of chemotherapy.

Dynamic 2-Deoxy-2-[18F]Fluoro-D-Glucose Positron Emission Tomography for Chemotherapy Response Monitoring of Breast Cancer Xenografts

PURPOSE

Non-invasive response monitoring can potentially be used to guide therapy selection for breast cancer patients. We employed dynamic 2-deoxy-2-[¹⁸F]fluoro-D-glucose positron emission tomography ([¹⁸F]FDG PET) to evaluate changes in three breast cancer xenograft lines in mice following three chemotherapy regimens.

PROCEDURES

Sixty-six athymic nude mice bearing bilateral breast cancer xenografts (two basal-like and one luminal-like subtype) underwent three 60 min [¹⁸F]FDG PET scans. Scans were performed prior to and 3 and 10 days after treatment with doxorubicin, paclitaxel, or carboplatin. Tumor growth was monitored in parallel. A pharmacokinetic compartmental model was fitted to the tumor uptake curves, providing estimates of transfer rates between the vascular, non-metabolized, and metabolized compartments. Early and late standardized uptake values (SUV_E and SUV_L, respectively); the rate constants k ₁, k ₂, and k ₃, and the intravascular fraction v _B were estimated. Changes in tumor volume were used as a response measure. Multivariate partial least-squares regression (PLSR) was used to assess if PET parameters could model tumor response and to identify PET parameters with the largest impact on response.

RESULTS

Treatment responders had significantly larger perfusion-related parameters (k ₁ and k ₂) and lower metabolism-related parameter (k ₃) than non-responders 10 days after the start of treatment. These findings were further supported by the PLSR analysis, which showed that k ₁ and k ₂ at day 10 and changes in k ₃ explained most of the variability in response to therapy, whereas SUV_L and particularly SUV_E were of lesser importance.

CONCLUSIONS

Overall, rate parameters related to both tumor perfusion and metabolism were associated with tumor response. Conventional metrics of [¹⁸F]FDG uptake such as SUV_E and SUV_L apparently had little relation to tumor response, thus necessitating full dynamic scanning and pharmacokinetic analysis for optimal evaluation of chemotherapy-induced changes in breast cancers.

Direct comparison of PET/CT and MRI to predict the pathological response to neoadjuvant chemotherapy in breast cancer: a meta-analysis

Both PET/CT and breast MRI are used to assess pathological complete response to neoadjuvant chemotherapy (NAC) in patients with breast cancer. The aim is to compare the utility of PET/CT and breast MRI by using head-to-head comparative studies. Literature databases were searched prior to July 2016. Eleven studies with a total of 527 patients were included. For PET/CT, the pooled SEN was 0.87 (95% confidence interval (CI): 0.71-0.95) and SPE was 0.85 (95% CI: 0.70-0.93). For MRI, the pooled SEN was 0.79 (95% CI: 0.68-0.87) and SPE was 0.82 (95% CI: 0.72-0.89). In the conventional contrast enhanced (CE)-MRI subgroup, PET/CT outperformed conventional CE-MRI with a higher pooled sensitivity (0.88 (95% CI: 0.71, 0.95) vs. 0.74 (95% CI: 0.60, 0.85), P = 0.018). In the early evaluation subgroup, PET/CT was superior to MRI with a notable higher pooled specificity (0.94 (95% CI: 0.78, 0.98) vs. 0.83 (95% CI: 0.81, 0.87), P = 0.015). The diagnostic performance of MRI is similar to that of PET/CT for the assessment of breast cancer response to NAC. However, PET/CT is more sensitive than conventional CE-MRI and more specific if the second imaging scan is performed before 3 cycles of NAC.

Breast PET/MR Imaging

Breast and whole-body PET/MR imaging is being used to detect local and metastatic disease and is being investigated for potential imaging biomarkers, which may eventually help personalize treatments and prognoses. This article provides an overview of breast and whole-body PET/MR exam techniques, summarizes PET and MR breast imaging for lesion detection, outlines investigations into multi-parametric breast PET/MR, looks at breast PET/MR in the setting of neo-adjuvant chemotherapy, and reviews the pros and cons of whole-body PET/MR in the setting of metastatic or suspected metastatic breast cancer.

Potential Clinical Applications of 18F-Fluorodeoxyglucose Positron Emission Tomography/Magnetic Resonance Mammography in Breast Cancer

The whole-body positron emission tomography (PET)/magnetic resonance (MR) scan is a cutting edge technology providing comprehensive structural information from MR imaging and functional features from PET in a single session. Recent research findings and clinical experience have shown that ¹⁸F-fluorodeoxyglucose (FDG) whole-body PET/MR imaging has a diagnostic performance comparable with or superior to that of PET/CT in the field of oncology, including for breast cancer. In particular, FDG PET/MR mammography in the prone position with the breast hanging in a pendant manner can provide more comprehensive information about the metabolism, anatomy, and functional features of a breast lesion than a whole-body PET/MR scan. This article reports on current state-of-the-art PET/MR mammography in patients with breast cancer and the prospects for potential application in the future.

FDG-PET/CT and MRI for Evaluation of Pathologic Response to Neoadjuvant Chemotherapy in Patients With Breast Cancer: A Meta-Analysis of Diagnostic Accuracy Studies

INTRODUCTION

This study compared the diagnostic test accuracy of magnetic resonance imaging (MRI) with that of (18)F-fluoro-2-glucose-positron emission tomography/computed tomography (FDG-PET/CT) imaging in assessment of response to neoadjuvant chemotherapy (NAC) in breast cancer.

METHODS

A systematic search was performed in PubMed and EMBASE (last updated in June 2015). Studies investigating the performance of MRI and FDG-PET or FDG-PET/CT imaging during or after completion of NAC in patients with histologically proven breast cancer were eligible for inclusion. We considered only studies reporting a direct comparison between these imaging modalities to establish precise summary estimates in the same setting of patients. Pathologic response was considered as the reference standard. Two authors independently screened and selected studies that met the inclusion criteria and extracted the data.

RESULTS

A total of 10 studies were included. The pooled estimates of sensitivity and specificity across all included studies were 0.71 and 0.77 for FDG-PET/CT (n = 535) and 0.88 and 0.55 for MRI (n = 492), respectively. Studies were subgrouped according to the time of therapy assessment. In the intra-NAC setting, FDG-PET/CT imaging outperformed MRI with fairly similar pooled sensitivity (0.91 vs. 0.89) and higher specificity (0.69 vs. 0.42). However, MRI appeared to have higher diagnostic accuracy than FDG-PET/CT imaging when performed after the completion of NAC, with significantly higher sensitivity (0.88 vs. 0.57).

CONCLUSION

Analysis of the available studies of patients with breast cancer indicates that the timing of imaging for NAC-response assessment exerts a major influence on the estimates of diagnostic accuracy. FDG-PET/CT imaging outperformed MRI in intra-NAC assessment, whereas the overall performance of MRI was higher after completion of NAC, before surgery.

IMPLICATIONS FOR PRACTICE

The timing of therapy assessment imaging exerts a major influence on overall estimates of diagnostic accuracy. (18)F-fluoro-2-glucose-positron emission tomography (FDG-PET)/computed tomography (CT) imaging outperformed magnetic resonance imaging (MRI) in intra-neoadjuvant chemotherapy assessment with fairly similar pooled sensitivity and higher specificity. However, MRI appeared to be more accurate than FDG-PET/CT in predicting pathologic response when used in the post-therapy setting.

Morphological and pathological response in primary systemic therapy of patients with breast cancer and the prediction of disease free survival: a single center observational study

AIM

To identify breast cancer subtypes likely to respond to primary systemic therapy (PST or neoadjuvant therapy) and to assess the accuracy of physical examination (PE) and breast ultrasonography (US) in evaluating and predicting residual size of breast carcinoma following PST.

METHODS

116 patients who received at least two cycles of PST between 1998 and 2009 were selected from a prospectively collected clinical database. Radiological assessment was done by mammography and US. Prior to PST, tumors were subclassified according to core biopsy (NCB) and/or fine-needle aspiration-based immunohistochemical profiles of NCB. Pathological response rates were assessed following the surgeries by using Chevallier classification. Tumor measurements by PE and US were obtained before and after PST. Different clinical measurements were compared with histological findings. Disease-free survival (DFS) was assessed.

RESULTS

Pathological complete remission (pCR=Chevallier I/II) was observed in 25 patients (21.5%), 44% of whom had triple negative histology, 28% Her2 positive and 76% had high-grade tumor. Of 116 patients, 24 received taxane-based PST, 48 combined taxane + anthracycline treatment, 8 trastuzumab combinations, 21 anthracycline-based treatments, and 15 other treatments. In the taxane treated group, the pCR rate was 30%, in the taxane + anthracycline group 25%, in the anthracycline group 9.5%, and in trastuzumab group 37.5%. After PST, PE and US were both significantly associated with pathology (P<0.001 and P=0.004, respectively). Concerning OS, significant difference was observed between the Chevallier III and IV group (P=0.031) in favor of Chevallier III group. In the pCR group, fewer events were observed during the follow-up period.

CONCLUSIONS

Our results show that even limited, routinely used immunohistochemical profiling of tumors can predict the likelihood of pCR to PST: patients with triple negative and Her2-positive cancers are more likely to achieve pCR to PST. Also, PE is better correlated with pathological findings than US.